Bibliographic record
Abstract
Over the past decade, developed countries have received significant numbers of North Korean asylum seekers. Some of these asylum seekers have managed to travel to developed countries without first travelling to South Korea. It has gradually become clear that many others are so-called ‘Saeteomin’ or ‘new settlers’, meaning North Koreans who have first settled in South Korea. This article will examine the law and policy response of destination countries to the influx of Saeteomin, especially focusing on the United States, the United Kingdom, and Canada. It will demonstrate that destination countries have reacted in three principal ways to the Saeteomin asylum seekers. First, they have evolved a more restrictive refugee jurisprudence in key areas affecting Saeteomin. Second, they have shared asylum seeker fingerprints with the South Korean authorities in an effort to help distinguish Saeteomin who deny having settled in South Korea from North Koreans who have not previously settled in South Korea. Third, the UK and Canada have attempted to deter Saeteomin asylum seekers through adding South Korea to safe country lists. This article argues that while these responses may be generally permissible under international law, they result in a number of problematic or potentially negative consequences. It will conclude by suggesting policy measures that South Korea and destination countries can take to better manage the issue of Saeteomin asylum seekers by adequately protecting the privacy of personal data and focusing on reducing the impetus to seek asylum outside South Korea.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.015 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".